**Adaptive Instance Normalization** is a normalization method that aligns the mean and variance of the content features with those of the style features. 

[Instance Normalization](https://paperswithcode.com/method/instance-normalization) normalizes the input to a single style specified by the affine parameters. Adaptive Instance Normaliation is an extension. In AdaIN, we receive a content input $x$ and a style input $y$, and we simply align the channel-wise mean and variance of $x$ to match those of $y$. Unlike [Batch Normalization](https://paperswithcode.com/method/batch-normalization), Instance Normalization or [Conditional Instance Normalization](https://paperswithcode.com/method/conditional-instance-normalization), AdaIN has no learnable affine parameters. Instead, it adaptively computes the affine parameters from the style input:

$$
\textrm{AdaIN}(x, y)= \sigma(y)\left(\frac{x-\mu(x)}{\sigma(x)}\right)+\mu(y)
$$

A **SENet** is a convolutional neural network architecture that employs squeeze-and-excitation blocks to enable the network to perform dynamic channel-wise feature recalibration.

SENet

Squeeze-and-Excitation Networks

Adaptive Instance Normalization

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

The **MLP-Mixer** architecture (or “Mixer” for short) is an image architecture that doesn't use convolutions or self-attention. Instead, Mixer’s architecture is based entirely on multi-layer perceptrons (MLPs) that are repeatedly applied across either spatial locations or feature channels. Mixer relies only on basic matrix multiplication routines, changes to data layout (reshapes and transpositions), and scalar nonlinearities.

It accepts a sequence of linearly projected image patches (also referred to as tokens) shaped as a “patches × channels” table as an input, and maintains this dimensionality. Mixer makes use of two types of MLP layers: channel-mixing MLPs and token-mixing MLPs. The channel-mixing MLPs allow communication between different channels; they operate on each token independently and take individual rows of the table as inputs. The token-mixing MLPs allow communication between different spatial locations (tokens); they operate on each channel independently and take individual columns of the table as inputs. These two types of layers are interleaved to enable interaction of both input dimensions.

Source	Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com